CP4285: Modern Recommendation Systems
Frequently Asked Questions (FAQ)

Frequently Asked Questions (FAQ)

For questions about lecture material, please use the relevant weekly discussion thread.

Administrivia

  1. What are the prerequisites for CP4285? The formal prerequisite is completion of at least 120 units. CS2109S (Introduction to AI and Machine Learning) or equivalent is also required. Students who have not taken CS2109S but have equivalent background in neural machine learning may seek approval from Min.

  2. What is the format of this course? Seminars are mandatory, physical face-to-face sessions. The course is taught by Kan Min-Yen. The class meets every Tuesday, 10:00–12:00, at Seminar Room 12, COM3-01-21. There are no tutorial sessions for this course.

  3. Will lectures be recorded? Lectures will be recorded where technology permits. Recordings are made available to facilitate revision only and are not a substitute for attendance. The standard NUS expectation is that all activities are face-to-face.

  4. I’m doing an ATAP/SIP/FYP related to recommendation systems. Am I allowed to take the course? Generally yes. However, if the course is oversubscribed, you will need to make an official appeal. Please contact Min directly if you have concerns about your eligibility.

  5. I’m on exchange. Can I take this course? Exchange students are welcome. Please ensure you meet the prerequisites and follow the standard NUS exchange enrolment process.

  6. Can I audit the course? Auditing is not officially supported. If you are interested, please contact Min.

Grading and Assessments

  1. How is the course graded? Please refer to the Grading page for the full breakdown: Final Exam (30%), Group Project (30%), Essays (20%), Quizzes/Tests (10%), Class Participation (10%).

  2. Are essays individual or group work? Essays are individual take-home assignments (each worth 10%). AI tools are permitted as a resource, but no collaboration with other students is allowed. You must submit an AI declaration with each essay; where requested by Min, you must provide full documentation of your AI use. Please refer to the Grading page for the full AI use policy.

  3. What is the group project about? Please refer to the Assignments page for full details. In brief, your team will select a recommendation system dataset, implement classical and neural baselines, evaluate them rigorously, and conduct an ethical analysis.

  4. How are project groups formed? You will first self-assemble into initial subgroups of 1–3 students via the Project Mini-team Declaration survey (due Week 5). Min will then assemble final groups of 5–6 students, taking project preferences and expertise into account.

  5. Will my participation grade be visible on Canvas? No. Your participation grade will not be available to you on Canvas until final grades are released.

  6. What happens if I miss the pre-flight or midterm survey? These surveys contribute to your participation marks. Missing them will result in a loss of those marks. Please complete them by their due dates.

Exam

  1. When is the final exam? The final exam is on Mon, 23 Nov 2026, 13:00–15:00 SGT. It is worth 30% of your total marks.

  2. What is the exam format? The exam will be conducted on the secure Examplify platform. Only Windows and MacOS laptops are supported (no tablets or iPads). The exam will consist mostly of MCQ, MRQ, and short answer questions. No AI tools are permitted during the exam.

  3. What if I don’t have a compatible laptop? You may request a loaner laptop from CTLT via the Loaner Laptop Quiz, which opens in Week 11. Note that the number of available laptops is limited.

Course Content and Prerequisites

  1. What are the prerequisites for CP4285? The formal prerequisite is completion of at least 120 units. CS2109S (Introduction to AI and Machine Learning) or equivalent is also required. Students who have not taken CS2109S but have equivalent background in neural machine learning may seek approval from Min.

  2. What programming language will we use? We will use Python 3.11 or newer, with libraries such as PyTorch, Scikit-learn, and RecBole for recommendation system tasks.

  3. Will we cover ethical issues in this course? Yes. Ethical considerations — including bias, fairness, privacy, exposure, transparency, and stakeholder impact — are interwoven throughout the curriculum as a core thread, not an afterthought.

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